2016 International Conference on Open Source Systems &Amp; Technologies (ICOSST) 2016
DOI: 10.1109/icosst.2016.7838580
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Deep belief networks for iris recognition based on contour detection

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Cited by 16 publications
(9 citation statements)
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“…Euclidean distance between these features are evaluated to calculate the similarity score. Bagar et al [1] proposed deep belief network based iris classification framework. The architecture is primarily inspired from those used for natural images, they are complex and deep which needs enough training data unlike iris datasets which lacks enormous training samples required for effective training of such complex nets.…”
Section: Deep Learning Approachesmentioning
confidence: 99%
“…Euclidean distance between these features are evaluated to calculate the similarity score. Bagar et al [1] proposed deep belief network based iris classification framework. The architecture is primarily inspired from those used for natural images, they are complex and deep which needs enough training data unlike iris datasets which lacks enormous training samples required for effective training of such complex nets.…”
Section: Deep Learning Approachesmentioning
confidence: 99%
“…Type of noise Proposed method Nixon and Aguado [65] Noise Median filter (MF) Huang et al [66] Blurriness Kernel estimation of the blur Lee et al [67] Reflections Line intensity profile (LIP) Santos and Hoyle [68] Low lighting (low illumination) CLAHE method Dehkordi and Abu-Bakar [69] Noise such as eyelids, eyelash, and light reflections and pupil pixels Multiple thresholding method Liu et al [70] Poor performance accuracy of low-resolution (LR) iris recognition A heterogeneous metric learning algorithm Raffei et al [71] Low contrast Adaptive histogram equalization Bakshi et al [72] Occlusions due to eyelashes and eyelids Gaussian filters and the Hough line detector Kumar et al [73] e difference between brighter and darker pixels Top-hat and bottom-hat filters Baqar et al [74] Specular highlight due to reflection 2D linear interpolation Djoumessi [75] Occlusions due to eyelids Hough line detector Gangwar et al [76] Specular reflection reshold Radman et al [77] Reflections Morphological-retinex method Ribeiro et al [78] Low resolution and quality of iris images Stacked autoencoder (SAE) technique and convolutional neural network (CNN) technique Arsenovic et al [79] Shadows on the eye images Histogram normalization Gad et al [80] Specular reflection Morphological operations Susitha and Subban [81] Low quality due to poor contrast CLAHE method Das and Derakhshani [82] Poor contrast and brightness BPDFHE method Donida et al [83] Specular reflections and noise Inpainting algorithm and Gaussian-based bilateral filter Complexity the iris image becomes more difficult due to different noise types such as the off-angle imaging, blurring, occlusion caused by eyelids and eyelashes, and specular reflection from illumination [104]. Raffei et al [105] proposed a fusion technique which consists of three substages.…”
Section: Sourcementioning
confidence: 99%
“…For the classification of iris pattern, Baqar et al [74] applied a technique which is dependent on a deep belief network (DBN) through restricted Boltzmann machine (RBM) with modified backpropagation algorithm-based feed-forward neural network (RVLR-NN) [74]. He et al introduced a method which used a deep belief network (DBN) to implement the iris classification [143].…”
Section: Classification Using Deep Learning Techniquesmentioning
confidence: 99%
“…In [149], Baqar and colleagues proposed an iris recognition framework based on deep belief networks, as well as contour information of iris images. Contour based feature vector has been used to discriminate samples belonging to different classes i.e., difference of sclerairis and iris-pupil contours, and is named as Unique Signature.…”
Section: Deep Learning Work On Iris Recognitionmentioning
confidence: 99%